Statistical method for monitoring manufacturing equipment and processing operations

ABSTRACT

A statistical process control method for monitoring and controlling semiconductor manufacturing processing operations is provided. For a chosen processing operation, multiple measurement sites are used to generate data of a measurable characteristic that is impacted by and associated with the processing operation. The data from the sites is compared over time and one or more outlier sites are identified. The outlier sites are the sites at which the data values are most divergent from the rest of the data. Algorithms are used to mathematically compare the outlier sites to the other sites to produce a comparative index. The comparative index is monitored graphically or otherwise to identify changes in the processing operation, and corrective actions are taken.

TECHNICAL FIELD

The disclosure relates, most generally, to semiconductor deviceprocessing operations and, more particularly, to statistical methods formonitoring and controlling such processing operations.

BACKGROUND

Process controls are used in the semiconductor manufacturing world.Semiconductor devices are formed by a multitude of processing operationscarried out upon a semiconductor or other substrate and each of theprocessing operations must be well controlled. The processing operationsshould exhibit run-to-run uniformity, i.e. process repeatability, anduniformity across the substrate being processed for each run. This isespecially true in today's rapidly advancing semiconductor manufacturingindustry in which device feature sizes are becoming smaller, thesubstrates upon which semiconductor devices are formed are becominglarger and a greater number of processing operations are used to formthe semiconductor devices that are increasing in complexity. It isimportant for the millions of features that are simultaneously formedacross the substrate to have the same dimensions and characteristicsthroughout the substrate each time a processing operation, i.e. run, iscarried out.

This applies to various different types of processing operations such asthermal operations, deposition operations, coating operations,implantation operations, etching operations, epitaxial growthoperations, polishing operations and various other operations used insemiconductor manufacturing.

It would therefore be advantageous to provide useful and reliablestatistical process control techniques and methods.

BRIEF DESCRIPTION OF THE DRAWING

The present disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawing. Itis emphasized that, according to common practice, the various featuresof the drawing are not necessarily to scale. On the contrary, thedimensions of the various features may be arbitrarily expanded orreduced for clarity. Like numerals denote like features throughout thespecification and drawing.

FIG. 1 is a flowchart illustrating a method according to an embodimentof the disclosure;

FIG. 2 shows embodiments of different numbers of sites that are measuredaccording to various embodiments of the disclosure;

FIG. 3A is a box plot, FIG. 3B is a plan view of a contour map and FIG.3C is a side view of a contour map used to identify an outlier site on asubstrate according to some embodiments of the disclosure;

FIGS. 4A-4C shows three contour plots in which an outlier zone isidentified according to some embodiments of the disclosure; and

FIGS. 5A and 5B are graphical representations of the application of analgorithm for monitoring and controlling processes data according tosome embodiments of the disclosure.

DETAILED DESCRIPTION

The disclosure relates to semiconductor device manufacturing and inparticular to semiconductor processing operations carried out to formintegrated circuits or other semiconductor devices upon semiconductor orother substrates using various processing tools. Embodiments of thedisclosure provide useful and reliable statistical process controltechniques and methods that can be applied to various processingoperations and which provide both run-to-run uniformity and within rununiformity information in which the statistical process control data canbe utilized to monitor and control semiconductor processing operations.The disclosure finds application in various semiconductor manufacturingprocess operations or processing tools in which the process operation iscarried out upon a substrate and has an impact on the substrate that canbe measured. Various embodiments of the disclosure provide forstatistical analysis and monitoring and statistical process control ofthe semiconductor processing operations.

The processing operation and processing tool are characterized bycarrying out a number of processing operations, also referred to as“runs”. For each run, data is collected from the processing operationsat a number of sites on the substrate being processed. The sites arechosen to be spread out at different locations on the substrate in orderto be representative of a characteristic of the processing operation atvarious locations on the substrate. The data is monitored at each of thesites for numerous runs over time and the data is analyzed according tovarious methods of the disclosure. Corrective action is identified andundertaken based on the data analysis.

According to an embodiment, the processing operation is an ionized metalplasma (IMP) titanium (Ti) deposition process but the methods of thedisclosure are applicable to other IMP deposition processes in otherembodiments. In still other embodiments, the methods of the disclosureare applied to various other deposition processes, epitaxial growthprocesses, coating processes, implantation or other dopant processingoperations or various semiconductor processing operations that have ameasurable impact upon the substrate being processed.

A characteristic that is impacted by the processing operation is chosenfor data recording and analysis. The processing operation has ameasurable impact upon a characteristic of the substrate beingprocessed. Various characteristics are used. In an embodiment, thecharacteristic is film thickness and the processing operation is a filmdeposition processing operation. In another embodiment, thecharacteristic associated with and impacted by a film depositionprocessing operation, is resistivity or sheet resistance. In otherembodiments, other processing operations are the subject of the methodsof the disclosure and the characteristic is resistivity, reflectivity,etching depth, amount of material removed, removal rate, dopantconcentration, various critical dimensions, and various othercharacteristics that are measurable by various metrology tools used insemiconductor manufacturing. In still other embodiments, thecharacteristic is a parameter that is measured electronically by circuittesting. Device speed, signal-to-noise ratio, and various othercharacteristics that are tested at WAT (wafer acceptance test) or otherelectrical or diagnostic tests in which the parameter tested isassociated with the performance of a processing operation. The data iscollected by measuring the characteristic at the multiple measurementsites on a substrate over many runs and then analyzed according tomethods and principles of the disclosure, and used to monitor, analyzeand control the associated processing operation or operations.

FIG. 1 is a flow chart showing steps of a method according to anembodiment of the disclosure. At “Perform multiple runs of processingoperation” step 3, various semiconductor processing operations arecarried out multiple times. Various semiconductor manufacturing toolsare used. In each run, one or multiple substrates is processed. Thesubstrates being processed vary from about 100 millimeters to about 300millimeters in diameter, in various embodiments. The processingoperations include deposition operations such as PVD (physical vapordeposition), CVD (chemical vapor deposition), PECVD (plasma enhancedchemical vapor deposition), sputtering operations, epitaxial growthoperations, and ionized metal plasma (IMP) deposition processes such asIMP titanium deposition processes. In other embodiments, the processingoperation is used to deposit various materials such as AlCu, TiN,cobalt, CoTi, tungsten, aluminum, copper, and various other materials ona substrate. In other embodiments, the processing operation is a thermaloperation such as a thermal growth operation or an annealing operationused to anneal a material, and in other embodiments, the processingoperation is an etching or stripping operation. In various otherembodiments, other processing operations such as low pressure TEOS(Tetraethly Orthosilicate) deposition, low pressure silicon nitridedeposition, polysilicon deposition, CMP (chemical mechanicalplanarization), rapid thermal anneal operations and variousphotolithography operations, are the subject of the statistical methodsof the disclosure.

At “Obtain data at multiple sites” step 5, data is obtained at each ofmultiple sites for each processing operation run. The data is obtainedby measuring a measurable characteristic such as film thickness,resistance, sheet resistivity, specularity, film density, step height,dopant density, refractive index (RI), stress, K (extinction coefficientindex), critical dimension (CD) or another characteristic impacted byand associated with the processing operation being analyzed. Variousmetrology or other measurement tools are used. The measurement methodand tool are determined by the processing operation being analyzed andthe characteristic associated with the processing operation that isbeing monitored and measured. Multiple sites on each substrate aremeasured. The sites are chosen to be spread out across the substrate.

FIG. 2 shows substrate 19 with multiple measurement sites. In anembodiment, the nine measurement sites 21 are used for data collectionand in another embodiment, a total of seventeen measurement sitesincluding measurement sites 21 and 23, are used for data collection.According to each embodiment, data is obtained by measuring the value ofa characteristic of the substrate at each of the measurement sites. Thecharacteristics are as described above. The measurement sites, whethernine measurement sites 21 are used or seventeen measurement sites 21 and23 are used, are chosen to be located at different positions throughoutsubstrate 19 and represent different zones on substrate 19. In otherembodiments, other numbers of measurement sites are used. The use ofnine measurement sites 21 and seventeen measurement sites 21 and 23represent two embodiments of the locations on a substrate used insemiconductor manufacturing that are used to provide data, but othernumbers of measurement sites are used in other embodiments.

Returning to FIG. 1, at “Identify outlier site” step 7, an outlier siteis identified. The data collected from each of the sites for each of theruns is analyzed and an outlier site is determined. The data is plottedor otherwise displayed, in some embodiments. The outlier site is thesite that is most statistically distinguished from the other sites andwill have the highest or lowest average value over the course of theprocessing operations studied. FIGS. 3A-3C and 4A-4C illustrate the stepof identifying the outlier site and are discussed below. In someembodiments, one outlier site is identified and in other embodiments,two outlier sites are identified. In some embodiments, multiple outliersites form a zone such as an annular zone on the substrate which isstatistically distinguished from other portions of the substrate.

At “Use algorithms to generate comparative index” step 9, an algorithmis used to produce a comparative index which is a representation of acomparison between the data values at the outlier site and the othermeasurement sites. Various algorithms are used. Various characteristicsare measured. The measured values of the characteristics are used in thealgorithms. In an embodiment, the comparative index represents themeasured value at the outlier site minus the average measured value ofthe other sites for each run. In another embodiment, the comparativeindex represents the measured value at the outlier site divided by theaverage of the measurement values of the other sites for each run. Instill another embodiment, the comparative index represents the measuredvalue at the outlier site minus the maximum measurement value obtainedat the other sites for each run. In still another embodiment, thecomparative index represents the measured value at the outlier sitedivided by the minimum measurement value obtained at the other sites foreach run. According to another embodiment, the comparative indexproduced by the algorithm represents a specific profile, unique to theprocess operation. In some embodiments, the comparative index is plottedor otherwise displayed and in other embodiments, various techniques areused to present the comparative index associated with each run overseveral runs carried out over time.

At “Use comparative index to monitor process operation” step 11, thecomparative index is studied to monitor a process, e.g. to determine howa process is operating such as run-to-run repeatability. In someembodiments, the comparative index is graphed for each run and changesin the value of the comparative index are indicative of changes in theconditions of the processing operation. FIGS. 5A and 5B are graphsshowing a plot of the comparative index according to various embodimentsof the disclosure and will be described later.

At “Control/adjust processing operation based on comparative index” step13, process control is carried out responsive to trends, changes orvalues in the comparative index. According to an embodiment, when thecomparative index changes, various corrective or other actions aretaken. In an embodiment, the corrective or other actions includeadjusting parameters of the processing operation. In another embodiment,the actions include analyzing the cause of the change in the comparativeindex and in some cases this change is attributable to various factorssuch as equipment malfunction or other system changes. The process andequipment is investigated to assess the change in the comparative indexand various further actions are taken.

FIGS. 3A-3C show how an outlier site or outlier sites or zone of outliersites, is determined. FIGS. 3A-3C represent a box plot and a contourplot of measured Ti thickness taken over time to establish and identifythe outlier site. In other embodiments, the data analysis is collectedfor other measurable characteristics associated with, and impacted by aprocessing operation. FIG. 3A is a box plot showing measured thicknessvalues of titanium in an IMP titanium deposition process. Sites 1through 9 were measured on a corresponding processed substrate for eachrun over a series of processing operation runs and the average measuredthickness at each of the sites is plotted in the box plot of FIG. 3A.Sites 1 through 9 appear along the x-axis and the values appearing alongthe x-axis and associated with each site, the average measured thicknessat that site. The average measure thickness is the center line in thebox that represents the data. At site 2 for example, the measuredaverage thickness 24 is identified within box 25 representing the dataat site 2. The outlier site is the site which has the most statisticallydivergent data, i.e. the most extreme deviation from the sample. Theoutlier site is a zone in some embodiments and is the zone with the mostunique data within the wafer. The outlier site has the highest or lowestaverage value over the runs and represents the site whose average valueis furthest distinguished from the average value of the other sites. Theaverage measured thickness 26 at site 1 is greatest average thicknessand is further distinguished from the average measured thickness at theother eight sites than the site with the lowest average thickness,identifying and establishing site 1 as the outlier site. In otherembodiments, the outlier site that has a value furthest from the averageof all the sites is a site with the lowest average value. As such, invarious embodiments (not shown), the outlier site is the site with thelowest measured value over time.

FIG. 3B and FIG. 3C are both contour maps. FIG. 3B represents datameasured over a number of runs with the average value plotted at varioussites on a substrate. The different shading is used to identify the “hotspot” or outlier site having a value most distinguished from the otherdata. In other embodiments, other graphical display techniques are usedto identify the outlier site. In FIG. 3C, a two-dimensional contour plottaken along a straight line along the surface of the substrate,identifies two outlier sites 27 which have values that are mostdistinguished from the data of the other sites. In an embodiment, thestraight line is line 28 in FIG. 3B but the data exhibited in FIG. 3C istaken at various other lines that extend across the data plot shown inFIG. 3B. In FIG. 3C, the two outlier sites 27 represent the highest datavalue. In some embodiments, the two outlier sites 27 represent twopoints that are part of a ring such as ring 29 of light shaded datashown in FIG. 3B. and form a zone with the most unique, or statisticallydivergent data. In other embodiments, other annular or other shapes onthe substrate surface form a zone with the most unique, or statisticallydivergent data.

FIGS. 4A-4C are contour plots in which the value of the measuredcharacteristic is plotted along the x direction along a direction on thesurface of the substrate and are also known as cross-section plots.FIGS. 4A-4C are contour plots that are similar to the plot in FIG. 3Cand represent data values taken along a straight line across asubstrate. FIGS. 4A-4C are general data plots and can represent anymeasurable data such as thickness, in various embodiments. In oneembodiment, FIGS. 4B-4C represent a contour plot (i.e. cross-sectionplot) representative of a single run or a contour plot with average datataken over a number of runs. FIG. 4A illustrates an embodiment in whicha singular location 41 at the highest inflection point along the plot isidentified as an outlier site. FIGS. 4B and 4C illustrate an embodimentin which two sites are identified as outlier sites and in someembodiments, the two outlier sites on FIGS. 4A-4C are indicative of acircular or annular zone on a substrate such as ring 29 shown in FIG.3B. Outlier sites 43 and 45 are shown in FIG. 4B and outlier sites areshown in FIG. 4C. FIGS. 4A-4C present a graphical technique foridentifying outlier sites with the highest or lowest inflection points.

FIGS. 5A and 5B are graphs in which a comparative index produced by analgorithm is plotted. After an outlier site is identified as above, acomparative index comparing the outlier sites to the other sites isgenerated using an algorithm, i.e. a mathematical relationship. Whenmultiple outlier sites are identified, a comparative index comparingeach of the outlier sites to the other sites is generated using analgorithm.

In an embodiment, the comparative index represents the value of theoutlier site minus the average value of other sites for each run. Inanother embodiment, the comparative index represents the value of theoutlier site divided by the average value of other sites for eachparticular run. In still another embodiment, the comparative indexrepresents the value at the outlier site minus the maximum value of allthe other data sites for each particular run. In still anotherembodiment, the comparative index represents the value at the outliersite minus the minimum value of all the other data sites for each run.The preceding are algorithms used in various embodiments, but otheralgorithms are used in other embodiments to produce other comparativeindices that are plotted for each run, such as in FIGS. 5A and 5B. Thedata, i.e. comparative index, is plotted for each run and monitored overtime to monitor the repeatability of the process. When changes in thedata are noted, the causes of the change are identified and variousadjustments are made and/or corrective actions taken. In someembodiments, the actions include troubleshooting the processing tooland/or changing the parameters of the processing operation to restorethe comparative index to its previous level.

FIG. 5A shows an embodiment in which an algorithm is used to generate acomparative index representative of the value (shown on the y axis) of“site 1” minus the average value of the other 8 sites for an embodimentin which 9 measurement sites are used. The x axis represents processingoperations carried out over time. Statistical process control methodsare used to generate upper control limits and lower control limits basedon the plotted comparative index data. Various statistical processcontrol methods for determining control limits are available and areused in various embodiments. The control limits are also displayed inFIG. 5A. Data points in circled region 31 lie outside the zoneidentified by the upper control limit UCL and lower control limit LCLindicating that the comparative index has changed relative to itshistorical levels and indicates a change in the processing operation. Insome embodiments, action is taken to correct the changes and identifythe cause of the change in the comparative index.

In some embodiments, each individual data point represents thecomparative index generated by measurements at multiple measurementsites on a single substrate, but in other embodiments, each individualdata point represents other sample sizes. In some embodiments, eachindividual data point is generated based on averages of the dataobtained from multiple substrates processed in sequence in a singlecontinuous event.

FIG. 5B is a graph plotting the comparative index representative of thevalue at the outlier site (“site 1”) minus the maximum value of theother 8 data points according to an embodiment in which 9 measurementsites are used. Various statistical process control techniques are usedto establish lower control limits LCL and upper control limits UCL whichare displayed in FIG. 5B. FIG. 5B shows at least two instances in whichthe comparative index is trending outside of the control limits. Thedata points in circled regions 33 and 35 are each indicative of a changein the processing conditions, as reflected by a change in the plottedcomparative index. In each case, the out-of-control data alertspersonnel to identify the cause of the change and to take appropriateaction to restore the comparative index to its historical, controlledlevels.

According to one aspect, a method for monitoring a manufacturingoperation is provided. The method comprises: performing multiple runs ofa processing operation upon semiconductor substrates over time, theprocessing operation having a measurable impact on a characteristic ofthe semiconductor substrate; measuring the characteristic at a pluralityof sites on the corresponding semiconductor substrate for each the run;identifying an outlier site of the plurality of sites wherein themeasured characteristic has the largest or smallest average value overthe runs; and generating a comparative index comparing a value of themeasured characteristics of the outlier site to a value of the measuredcharacteristics of other sites of the plurality of sites, for each therun.

In some embodiments, the method further comprises adjusting parametersof the processing operation based upon the comparative index.

In some embodiments, the method further comprises performing furtherruns of the processing operation and measuring the characteristic at theoutlier site and at the other sites for each the further run; andgenerating the comparative index for each the further run and monitoringthe comparative index in time.

In some embodiments, the method further comprises controlling theprocessing operation by adjusting a parameter thereof based on changesin the comparative index.

In some embodiments, the comparative index represents the value of themeasured characteristic of the outlier site minus an average value ofthe measured characteristic of the other sites.

In some embodiments, the comparative index represents the value of themeasured characteristic of the outlier site divided by an average valueof the measured characteristic of the other sites.

In some embodiments, the comparative index represents the value of themeasured characteristic of the outlier site minus a maximum value of themeasured characteristics of the other sites, for each the run.

In some embodiments, the processing operation comprises an ionized metalplasma (IMP) deposition operation and the characteristic comprises filmthickness.

In some embodiments, the plurality of sites comprise nine or seventeensites and the characteristic comprises resistivity.

According to one aspect, a method for monitoring a manufacturingoperation is provided. The method comprises performing multiple runs ofa processing operation upon semiconductor substrates over time, theprocessing operation having a measurable impact on a characteristic;measuring the characteristic at a plurality of sites on thecorresponding semiconductor substrate for each the run; identifying oneor more outlier sites of the plurality of sites with the moststatistically divergent data of the measured characteristic over theruns; and generating a comparative index comparing a value of themeasured characteristics of each of the one or more outlier sites to avalue of the measured characteristics of other sites of the plurality ofsites, for each the run.

In some embodiments, the one or more outlier sites comprise an annularzone on the substrates.

In some embodiments, the comparative index represents one of: the valueof the measured characteristic of each of the one or more outlier sitesminus an average value of the measured characteristic of the othersites; and the value of the measured characteristic of each of the oneor more outlier sites divided by an average value of the measuredcharacteristic of the other sites.

According to one aspect, a method for monitoring a manufacturingoperation, is provided. The method comprises performing multiple runs ofa processing operation upon semiconductor substrates over time, theprocessing operation having a measurable impact on a characteristic. Themethod also includes measuring the characteristic at a plurality ofsites on the corresponding semiconductor substrate for each the run;identifying an outlier site of the plurality of sites in which themeasured characteristic has the largest or smallest average value overthe runs; performing further runs of the processing operation on furthersemiconductor substrates and measuring the characteristic at the outliersite and at other sites of the plurality of sites on the correspondingfurther semiconductor substrate, for each the further run; and comparinga value of the measured characteristic of the outlier site to a value ofthe measured characteristic of the other sites of each the further run,to control the processing operation.

In some embodiments, the comparing comprises mathematically comparingusing an algorithm and further comprising controlling the processingoperation by adjusting a parameter thereof based on the comparing.

In some embodiments, the comparing produces a comparative index thatcomprises the value of the measured characteristic of the outlier siteto the value of the measured characteristic of the other sites andwherein the comparing includes monitoring the comparative index overtime.

In some embodiments, the method further comprises adjusting theprocessing operation by adjusting at least one parameter of theprocessing operation based on the change when the comparative indexchanges.

In some embodiments, the comparative index represents the value of themeasured characteristic of the outlier site minus an average value ofthe measured characteristic of the other sites.

In some embodiments, the comparative index represents the value of themeasured characteristic of the outlier site divided by an average valueof the measured characteristic of the other sites.

In some embodiments, the comparing produces a comparative index thatrepresents the value of the measured characteristic of the outlier siteminus a maximum value of the values of the measured characteristic ofthe other sites and wherein the comparing comprises monitoring thecomparative index over time.

In some embodiments, the processing operation comprises an ionized metalplasma (IMP) deposition operation and the characteristic comprises filmthickness.

The preceding merely illustrates the principles of the disclosure. Itwill thus be appreciated that those of ordinary skill in the art will beable to devise various arrangements which, although not explicitlydescribed or shown herein, embody the principles of the disclosure andare included within its spirit and scope. Furthermore, all examples andconditional language recited herein are principally intended expresslyto be only for pedagogical purposes and to aid the reader inunderstanding the principles of the disclosure and the conceptscontributed by the inventors to furthering the art, and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the disclosure, as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents and equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

This description of the exemplary embodiments is intended to be read inconnection with the figures of the accompanying drawing, which are to beconsidered part of the entire written description. In the description,relative terms such as “lower,” “upper,” “horizontal,” “vertical,”“above,” “below,” “up,” “down,” “top” and “bottom” as well asderivatives thereof (e.g., “horizontally,” “downwardly,” “upwardly,”etc.) should be construed to refer to the orientation as then describedor as shown in the drawing under discussion. These relative terms arefor convenience of description and do not require that the apparatus beconstructed or operated in a particular orientation. Terms concerningattachments, coupling and the like, such as “connected” and“interconnected,” refer to a relationship wherein structures are securedor attached to one another either directly or indirectly throughintervening structures, as well as both movable or rigid attachments orrelationships, unless expressly described otherwise.

Although the disclosure has been described in terms of exemplaryembodiments, it is not limited thereto. Rather, the appended claimsshould be construed broadly, to include other variants and embodimentsof the disclosure, which may be made by those of ordinary skill in theart without departing from the scope and range of equivalents of thedisclosure.

What is claimed is:
 1. A method for monitoring a manufacturingoperation, said method comprising: performing multiple runs of aprocessing operation upon semiconductor substrates over time, saidprocessing operation having a measurable impact on a characteristic ofsaid semiconductor substrate; measuring said characteristic at aplurality of sites on said corresponding semiconductor substrate foreach said run; identifying an outlier site of said plurality of siteswherein said measured characteristic has the largest or smallest averagevalue over said runs; generating a comparative index comparing a valueof said measured characteristics of said outlier site to a value of saidmeasured characteristics of other sites of said plurality of sites, foreach said run; performing further runs of said processing operation andmeasuring said characteristic at said outlier site and at said othersites for each said further run; and generating said comparative indexfor each said further run and monitoring said comparative index in time;and controlling said processing operation by adjusting a parameterthereof based on changes in said comparative index.
 2. The method as inclaim 1, further comprising adjusting parameters of said processingoperation based upon said comparative index.
 3. The method as in claim1, wherein said comparative index represents said value of said measuredcharacteristic of said outlier site minus an average value of saidmeasured characteristic of said other sites.
 4. The method as in claim1, wherein said comparative index represents said value of said measuredcharacteristic of said outlier site divided by an average value of saidmeasured characteristic of said other sites.
 5. The method as in claim1, wherein said comparative index represents said value of said measuredcharacteristic of said outlier site minus a maximum value of saidmeasured characteristics of said other sites, for each said run.
 6. Themethod as in claim 1, wherein said processing operation comprises anionized metal plasma (IMP) deposition operation and said characteristiccomprises film thickness.
 7. The method as in claim 1, wherein saidplurality of sites comprise nine or seventeen sites and saidcharacteristic comprises resistivity.
 8. A method for monitoring amanufacturing operation, said method comprising: performing multipleruns of a processing operation upon semiconductor substrates over time,said processing operation having a measurable impact on a characteristicof said semiconductor substrate; measuring said characteristic at aplurality of sites on said corresponding semiconductor substrate foreach said run; identifying one or more outlier sites of said pluralityof sites with the most statistically divergent data of said measuredcharacteristic over said runs; and generating a comparative indexcomparing a value of said measured characteristics of each of said oneor more outlier sites to a value of said measured characteristics ofother sites of said plurality of sites, for each said run; wherein saidone or more outlier sites comprise an annular zone on said substrates;wherein said comparative index represents one of: said value of saidmeasured characteristic of each of said one or more outlier sites minusan average value of said measured characteristic of said other sites;and said value of said measured characteristic of each of said one ormore outlier sites divided by an average value of said measuredcharacteristic of said other sites.
 9. A method for monitoring amanufacturing operation, said method comprising: performing multipleruns of a processing operation upon semiconductor substrates over time,said processing operation having a measurable impact on acharacteristic; measuring said characteristic at a plurality of sites onsaid corresponding semiconductor substrate for each said run;identifying an outlier site of said plurality of sites in which saidmeasured characteristic has the largest or smallest average value oversaid runs; performing further runs of said processing operation onfurther semiconductor substrates and measuring said characteristic atsaid outlier site and at other sites of said plurality of sites on saidcorresponding further semiconductor substrate, for each said furtherrun; and comparing a value of said measured characteristic of saidoutlier site to a value of said measured characteristic of said othersites of each said further run, to control said processing operation;wherein said comparing produces a comparative index that comprises saidvalue of said measured characteristic of said outlier site to said valueof said measured characteristic of said other sites and wherein saidcomparing includes monitoring said comparative index over time.
 10. Themethod as in claim 9, wherein said comparing comprises mathematicallycomparing and further comprising controlling said processing operationby adjusting a parameter thereof based on said comparing.
 11. The methodas in claim 9, further comprising adjusting said processing operation byadjusting at least one parameter of said processing operation based on achange of said comparative index.
 12. The method as in claim 9, whereinsaid comparative index represents said value of said measuredcharacteristic of said outlier site minus an average value of saidmeasured characteristic of said other sites.
 13. The method as in claim9, wherein said comparative index represents said value of said measuredcharacteristic of said outlier site divided by an average value of saidmeasured characteristic of said other sites.
 14. The method as in claim9, wherein said comparing produces a comparative index that representssaid value of said measured characteristic of said outlier site minus amaximum value of said values of said measured characteristic of saidother sites and wherein said comparing comprises monitoring saidcomparative index over time.
 15. The method as in claim 9, wherein saidprocessing operation comprises an ionized metal plasma (IMP) depositionoperation and said characteristic comprises film thickness.